Diff of Vector Indexing at 6d183e5

@@ -4,2 +4,4 @@
 * product quantization (lossy compression)
-* inverted lists (split vectors into clusters, search a subset of the clusters)
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+* inverted lists (split vectors into clusters, search a subset of the clusters)
+
+Inverted list/product quantization was historically the most common way to search large vector datasets. However, recall is very bad in some circumstances (most notably when query/dataset vectors are drawn from significantly different distributions: see [[https://arxiv.org/abs/2305.04359]] and [[https://kay21s.github.io/RoarGraph-VLDB2024.pdf]]. The latter explains this phenomenon as resulting from the nearest neighbours being split across many more (and more widely distributed) clusters (cells) than with in-distribution queries.
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